sarkas.tools.observables.StaticStructureFactor#

class sarkas.tools.observables.StaticStructureFactor[source]#

Static Structure Factors.

The species dependent SSF \(S_{AB}(\mathbf k)\) is calculated from

\[S_{AB}(\mathbf k) = \int_0^\infty dt \, \left \langle | n_{A}( \mathbf k, t)n_{B}( -\mathbf k, t) \right \rangle,\]

where the microscopic density of species \(A\) with number of particles \(N_{A}\) is given by

\[n_{A}(\mathbf k,t) = \sum^{N_{A}}_{j} e^{-i \mathbf k \cdot \mathbf r_j(t)} .\]
Variables

  • k_list (list) – List of all possible \(k\) vectors with their corresponding magnitudes and indexes.

  • k_counts (numpy.ndarray) – Number of occurrences of each \(k\) magnitude.

  • ka_values (numpy.ndarray) – Magnitude of each allowed \(ka\) vector.

  • no_ka_values (int) – Length of ka_values array.

Methods

StaticStructureFactor.__init__()

StaticStructureFactor.average_slices_data()

Calculate the average and standard deviation of the observable from the slices dataframe.

StaticStructureFactor.calc_k_data()

Calculate and save Fourier space data.

StaticStructureFactor.calc_nkt_slices_data()

Calculate n(k,t) for each slice.

StaticStructureFactor.calc_slices_data()

Calculate the observable for each slice.

StaticStructureFactor.calc_vkt_slices_data()

Calculate v(k,t) for each slice.

StaticStructureFactor.calculate_corr_times([...])

StaticStructureFactor.compute()

Routine for computing the observable.

StaticStructureFactor.compute_kt_data([...])

Calculate Time dependent Fourier space quantities.

StaticStructureFactor.copy_params(params)

StaticStructureFactor.create_dirs_filenames()

Create the directories and filenames where to save dataframes.

StaticStructureFactor.from_dict(input_dict)

Update attributes from input dictionary.

StaticStructureFactor.from_pickle()

Read the observable's info from the pickle file.

StaticStructureFactor.grab_sim_data([pva])

Read in particles data into one large array.

StaticStructureFactor.initialize_hdf()

StaticStructureFactor.integrate_normalized_acf_squared(...)

Calculate the normalized correlation time as given by

StaticStructureFactor.parse([acf_data])

Grab the pandas dataframe from the saved csv file.

StaticStructureFactor.parse_acf()

StaticStructureFactor.parse_k_data()

Read in the precomputed Fourier space data.

StaticStructureFactor.parse_kt_data([...])

Read in the precomputed time dependent Fourier space data.

StaticStructureFactor.plot([scaling, acf, ...])

Plot the observable by calling the pandas.DataFrame.plot() function and save the figure.

StaticStructureFactor.pretty_print_msg()

Create the message with the basic information of every observable

StaticStructureFactor.save_acf_hdf()

StaticStructureFactor.save_hdf()

StaticStructureFactor.save_kt_hdf([...])

Save the \(n(\mathbf{k},t)\) and/or \(\mathbf{v}(\mathbf{k},t)\) data of each slice to disk.

StaticStructureFactor.save_pickle()

Save the observable's info into a pickle file.

StaticStructureFactor.setup(params[, phase, ...])

Assign attributes from simulation's parameters.

StaticStructureFactor.setup_init(params[, ...])

Assign Observables attributes and copy the simulation's parameters.

StaticStructureFactor.setup_multirun_dirs()

Set the attributes postprocessing_dir and dump_dirs_list.

StaticStructureFactor.update_args(**kwargs)

Update observable specific attributes and call update_finish() to save info.

StaticStructureFactor.update_finish()

Update the slice_steps, CCF's and DSF's attributes, and save pickle file with observable's info.